Evidence map›Paper›PMID 36260399›Full record

ArticleJournal of medical Internet research2021

US Privacy Laws Go Against Public Preferences and Impede Public Health and Research: Survey Study.

Cason Schmit, Theodoros Giannouchos, Mahin Ramezani, Qi Zheng, Michael A Morrisey, Hye-Chung Kum

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Cason Schmit *Population Informatics Lab, Department of Health Policy and Management, Texas A&M University, College Station, TX, United States.ORCID 0000-0002-7929-7198
Theodoros GiannouchosPopulation Informatics Lab, Department of Health Policy and Management, Texas A&M University, College Station, TX, United States.ORCID 0000-0002-1574-6767
Mahin RamezaniPopulation Informatics Lab, Department of Health Policy and Management, Texas A&M University, College Station, TX, United States.ORCID 0000-0002-2659-6151
Qi ZhengDepartment of Epidemiology and Biostatistics, Texas A&M University, College Station, TX, United States.ORCID 0000-0003-3920-2059
Michael A MorriseyPopulation Informatics Lab, Department of Health Policy and Management, Texas A&M University, College Station, TX, United States.ORCID 0000-0001-9357-1141
Hye-Chung Kum *Population Informatics Lab, Department of Health Policy and Management, Texas A&M University, College Station, TX, United States.ORCID 0000-0002-6882-8053

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundReaping the benefits from massive volumes of data collected in all sectors to improve population health, inform personalized medicine, and transform biomedical research requires the delicate balance between the benefits and risks of using individual-level data. There is a patchwork of US data protection laws that vary depending on the type of data, who is using it, and their intended purpose. Differences in these laws challenge big data projects using data from different sources. The decisions to permit or restrict data uses are determined by elected officials; therefore, constituent input is critical to finding the right balance between individual privacy and public benefits.

objectiveThis study explores the US public's preferences for using identifiable data for different purposes without their consent.

methodsWe measured data use preferences of a nationally representative sample of 504 US adults by conducting a web-based survey in February 2020. The survey used a choice-based conjoint analysis. We selected choice-based conjoint attributes and levels based on 5 US data protection laws (Health Insurance Portability and Accountability Act, Family Educational Rights and Privacy Act, Privacy Act of 1974, Federal Trade Commission Act, and the Common Rule). There were 72 different combinations of attribute levels, representing different data use scenarios. Participants were given 12 pairs of data use scenarios and were asked to choose the scenario they were the most comfortable with. We then simulated the population preferences by using the hierarchical Bayes regression model using the ChoiceModelR package in R.

resultsParticipants strongly preferred data reuse for public health and research than for profit-driven, marketing, or crime-detection activities. Participants also strongly preferred data use by universities or nonprofit organizations over data use by businesses and governments. Participants were fairly indifferent about the different types of data used (health, education, government, or economic data).

conclusionsOur results show a notable incongruence between public preferences and current US data protection laws. Our findings appear to show that the US public favors data uses promoting social benefits over those promoting individual or organizational interests. This study provides strong support for continued efforts to provide safe access to useful data sets for research and public health. Policy makers should consider more robust public health and research data use exceptions to align laws with public preferences. In addition, policy makers who revise laws to enable data use for research and public health should consider more comprehensive protection mechanisms, including transparent use of data and accountability.

Indexed as

ConfidentialityPrivacyAdultBayes TheoremHealth Insurance Portability and Accountability ActHumansPublic HealthUnited Statesbig dataconjoint analysishealth policyinformation disseminationlawmedical informaticsprivacypublic healthpublic policysurveys and questionnaires

Identifiers

PMID36260399
PMCPMC8406123

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.